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This research paper presents to study and analyzed the cost economics, for an investment to produce electricity with renewable energy of water from weir, and measurement of water pressure due to water conservation of community village area. In case study of the hydropower plant project, amphoe Makham, Chanthaburi province in Thailand, which this research will be design and test for electricity generation from hydropower, will be the real testing weir and considering based on the economics cost estimation. Analysis of economic and financial with selective approach to research and development projects that is appropriate to the economy. Thus, evaluation of economics cost indicators including the calculation of net present value, internal rate of return of this project (IRR), analysis of return on investment, benefit-cost ratio (B/C), average incremental costs or cost of energy, pay-back period time and cash flow value, etc. The results of measurement from water pressure and analyze the cost economics are will enough to bring to produce the electricity. The hydropower turbine size 7 MW have potential in electricity production is 38.45 GWh/year for economics evaluation meets that can payback get in period time is 12 year 5 month, the benefit-cost ratio on the investment is 1.34 and yield of the project or internal rate return to 13.28 %. 

This paper presents the development an Adaptive Neuro Fuzzy inference control system for purpose of improving power system is an application of Artificial Neural Network (ANN) and Fuzzy Logic based hourly load demand forecasting with linear polynomial and exponential equation. The ANN involved is designed using the multilayer back propagation learning. The Fuzzy Logic and the ANN input layer receives information on next day maximum temperature, period and hourly load. The class of day type, the hourly load in present day. The Fuzzy Logic and the ANN output layer provides the predicted hourly load. Test is performed using data of hourly load of Bangkok forecasting for 24 days. The results show that ANN model has mean absolute percentage error of 1.7 % and the Fuzzy Logic model has mean absolute percentage error of 1.5 %. The accurate results of the forecasting will improve the power system security and save generation cost.Index Terms -Hourly load demandforecasting, power system, adaptive Neuro-Fuzzy inference, next day load.

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